Machine learning Tools

Machine learning 

Gives a set of tools that use computers to transform data into actionable information. Tools are a big part of machine learning and choosing the right tool can be as important as working with the best algorithms. Machine learning tools can apply machine learning faster, easier. Good tools can automate each step in the applied machine learning process by shortening the time.

   The machine learning tools are as follows-

• Platforms-

               Platforms are used to completed a machine learning project from beginning to end.

1.  provide capabilities required at each step in a machine learning project.
2. The interface may ve graphical or command line. 
3. They provide a loose coupling of features.
4. They are provided for general purpose use and exploration rather than speed, scalability,or accuracy.

• Library- Library gives capabilities for completing part of the machine learning project.

1. Provide a specific capability for one or more steps in a machine learning project.
2. The interface is typically an application programming interface requiring programming.
3.  They are tailored for a specific use case, problem type,or environment.

• Graphical User Interfaces- 

1. It allows less-technical users to work through machine learning. 
2. Focus on the procedure how to get the most from machine learning techniques.
3. A stronger focus on graphical presentations of information such as visualization.
4. Structured process imposed on the user by the interface.

• Command Line Interface- 

1. Allows technical users who are not programmers to work through machine learning projects.
2. Frames machine learning tasks in terms of the input required and output to be generated.
3. Promotes reproducible results by recording or scripting commands and command-line arguments.

• Application Programming Interfaces-

1. To incorporate machine learning into our own software projects.
2. To create our own machine learning tools. 
3. Gives the flexibility to use our own processes and automation on machine learning projects.
4. Allows combining our own methods with those provided by the library as well as extending provided methods.

• Local Tools- Local tools can be downloaded, installed, and run on a local environment

1. .Customized for in-memory data and algorithms.
2. Control over run own systems to meet our needs.

• Remote Tools- Remote tools can be hosted on a server and called from local enve=ironment. These tools are often referred to as machine learning as a service(MLaaS)